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Pipe breaks and estimating the impact of pressure control in water supply networks

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  • Jara-Arriagada, Carlos
  • Stoianov, Ivan

Abstract

The deterioration and fracture of water supply pipes present a major threat for the continuous provision of drinking water. The hydraulic pressure in pipes is an influential factor for the occurrence of pipe breaks. However, little evidence has been provided so far for the quantitative assessment of the impact of pressure control on reducing the number of pipe breaks. In this paper, we applied logistic regression with polynomial terms, and a sensitivity analysis to assess the potential impact of pressure control on reducing pipe breaks. A large dataset of historic pipe breaks was used to develop and validate the presented method. Cast iron and asbestos cement pipes were examined in detail. Results showed that pipe breaks could be decreased by 18% to 30% by reducing the mean pressure for the investigated cohorts of asbestos cement and cast iron pipes. Pressure range reduction could provide larger impacts on both pipe materials. These results indicate that proactively controlling the hydraulic pressure may have a potentially significant impact on the reliability and sustainability of water supply networks.

Suggested Citation

  • Jara-Arriagada, Carlos & Stoianov, Ivan, 2021. "Pipe breaks and estimating the impact of pressure control in water supply networks," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:reensy:v:210:y:2021:i:c:s0951832021000843
    DOI: 10.1016/j.ress.2021.107525
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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Xu, Qiang & Chen, Qiuwen & Li, Weifeng & Ma, Jinfeng, 2011. "Pipe break prediction based on evolutionary data-driven methods with brief recorded data," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 942-948.
    3. Robles-Velasco, Alicia & Cortés, Pablo & Muñuzuri, Jesús & Onieva, Luis, 2020. "Prediction of pipe failures in water supply networks using logistic regression and support vector classification," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    4. Yamijala, Shridhar & Guikema, Seth D. & Brumbelow, Kelly, 2009. "Statistical models for the analysis of water distribution system pipe break data," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 282-293.
    5. Iman Moslehi & Mohammadreza Jalili_Ghazizadeh, 2020. "Pressure-Pipe Breaks Relationship in Water Distribution Networks: A Statistical Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2851-2868, July.
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    Cited by:

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    2. Jia, Rui & Du, Kun & Song, Zhigang & Xu, Wei & Zheng, Feifei, 2024. "Scenario reduction-based simulation method for efficient serviceability assessment of earthquake-damaged water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
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    4. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. Ramos-Salgado, Cristóbal & Muñuzuri, Jesús & Aparicio-Ruiz, Pablo & Onieva, Luis, 2021. "A decision support system to design water supply and sewer pipes replacement intervention programs," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Rifaai, Talha M. & Abokifa, Ahmed A. & Sela, Lina, 2022. "Integrated approach for pipe failure prediction and condition scoring in water infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    7. Omar Abdulah Shrrat Omar, 2023. "Evaluation of Pipe Materials in Water System Networks Using the Theory of Advanced Multi-Criteria Analysis," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
    8. Ramos-Salgado, Cristóbal & Muñuzuri, Jesús & Aparicio-Ruiz, Pablo & Onieva, Luis, 2022. "A comprehensive framework to efficiently plan short and long-term investments in water supply and sewer networks," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    9. Daulat, Shamsuddin & Rokstad, Marius Møller & Bruaset, Stian & Langeveld, Jeroen & Tscheikner-Gratl, Franz, 2024. "Evaluating the generalizability and transferability of water distribution deterioration models," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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